diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst
index dc151871874d4..69045147d8af9 100644
--- a/doc/modules/preprocessing.rst
+++ b/doc/modules/preprocessing.rst
@@ -941,6 +941,7 @@ learned in :meth:`~TargetEncoder.fit_transform`.
 .. topic:: Examples:
 
   * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder.py`
+  * :ref:`sphx_glr_auto_examples_preprocessing_plot_target_encoder_cross_val.py`
 
 .. topic:: References
 
diff --git a/examples/preprocessing/plot_target_encoder_cross_val.py b/examples/preprocessing/plot_target_encoder_cross_val.py
new file mode 100644
index 0000000000000..455625cc47460
--- /dev/null
+++ b/examples/preprocessing/plot_target_encoder_cross_val.py
@@ -0,0 +1,160 @@
+"""
+==========================================
+Target Encoder's Internal Cross Validation
+==========================================
+
+.. currentmodule:: sklearn.preprocessing
+
+The :class:`TargetEnocoder` replaces each category of a categorical feature with
+the mean of the target variable for that category. This method is useful
+in cases where there is a strong relationship between the categorical feature
+and the target. To prevent overfitting, :meth:`TargetEncoder.fit_transform` uses
+interval cross validation to encode the training data to be used by a downstream
+model. In this example, we demonstrate the importance of the cross validation
+procedure to prevent overfitting.
+"""
+
+# %%
+# Create Synthetic Dataset
+# ========================
+# For this example, we build a dataset with three categorical features: an informative
+# feature with medium cardinality, an uninformative feature with medium cardinality,
+# and an uninformative feature with high cardinality. First, we generate the informative
+# feature:
+from sklearn.preprocessing import KBinsDiscretizer
+import numpy as np
+
+n_samples = 50_000
+
+rng = np.random.RandomState(42)
+y = rng.randn(n_samples)
+noise = 0.5 * rng.randn(n_samples)
+n_categories = 100
+
+kbins = KBinsDiscretizer(
+    n_bins=n_categories, encode="ordinal", strategy="uniform", random_state=rng
+)
+X_informative = kbins.fit_transform((y + noise).reshape(-1, 1))
+
+# Remove the linear relationship between y and the bin index by permuting the values of
+# X_informative
+permuted_categories = rng.permutation(n_categories)
+X_informative = permuted_categories[X_informative.astype(np.int32)]
+
+# %%
+# The uninformative feature with medium cardinality is generated by permuting the
+# informative feature and removing the relationship with the target:
+X_shuffled = rng.permutation(X_informative)
+
+# %%
+# The uninformative feature with high cardinality is generated so that is independent of
+# the target variable. We will show that target encoding without cross validation will
+# cause catastrophic overfitting for the downstream regressor. These high cardinality
+# features are basically unique identifiers for samples which should generally be
+# removed from machine learning dataset. In this example, we generate them to show how
+# :class:`TargetEncoder`'s default cross validation behavior mitigates the overfitting
+# issue automatically.
+X_near_unique_categories = rng.choice(
+    int(0.9 * n_samples), size=n_samples, replace=True
+).reshape(-1, 1)
+
+# %%
+# Finally, we assemble the dataset and perform a train test split:
+from sklearn.model_selection import train_test_split
+import pandas as pd
+
+X = pd.DataFrame(
+    np.concatenate(
+        [X_informative, X_shuffled, X_near_unique_categories],
+        axis=1,
+    ),
+    columns=["informative", "shuffled", "near_unique"],
+)
+X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
+
+# %%
+# Training a Ridge Regressor
+# ==========================
+# In this section, we train a ridge regressor on the dataset with and without
+# encoding and explore the influence of target encoder with and without the
+# interval cross validation. First, we see the Ridge model trained on the
+# raw features will have low performance, because the order of the informative
+# feature is not informative:
+from sklearn.linear_model import Ridge
+import sklearn
+
+# Configure transformers to always output DataFrames
+sklearn.set_config(transform_output="pandas")
+
+ridge = Ridge(alpha=1e-6, solver="lsqr", fit_intercept=False)
+
+raw_model = ridge.fit(X_train, y_train)
+print("Raw Model score on training set: ", raw_model.score(X_train, y_train))
+print("Raw Model score on test set: ", raw_model.score(X_test, y_test))
+
+# %%
+# Next, we create a pipeline with the target encoder and ridge model. The pipeline
+# uses :meth:`TargetEncoder.fit_transform` which uses cross validation. We see that
+# the model fits the data well and generalizes to the test set:
+from sklearn.pipeline import make_pipeline
+from sklearn.preprocessing import TargetEncoder
+
+model_with_cv = make_pipeline(TargetEncoder(random_state=0), ridge)
+model_with_cv.fit(X_train, y_train)
+print("Model with CV on training set: ", model_with_cv.score(X_train, y_train))
+print("Model with CV on test set: ", model_with_cv.score(X_test, y_test))
+
+# %%
+# The coefficients of the linear model shows that most of the weight is on the
+# feature at column index 0, which is the informative feature
+import pandas as pd
+import matplotlib.pyplot as plt
+
+plt.rcParams["figure.constrained_layout.use"] = True
+
+coefs_cv = pd.Series(
+    model_with_cv[-1].coef_, index=model_with_cv[-1].feature_names_in_
+).sort_values()
+_ = coefs_cv.plot(kind="barh")
+
+# %%
+# While :meth:`TargetEncoder.fit_transform` uses an interval cross validation,
+# :meth:`TargetEncoder.transform` itself does not perform any cross validation.
+# It uses the aggregation of the complete training set to transform the categorical
+# features. Thus, we can use :meth:`TargetEncoder.fit` followed by
+# :meth:`TargetEncoder.transform` to disable the cross validation. This encoding
+# is then passed to the ridge model.
+target_encoder = TargetEncoder(random_state=0)
+target_encoder.fit(X_train, y_train)
+X_train_no_cv_encoding = target_encoder.transform(X_train)
+X_test_no_cv_encoding = target_encoder.transform(X_test)
+
+model_no_cv = ridge.fit(X_train_no_cv_encoding, y_train)
+
+# %%
+# We evaluate the model on the non-cross validated encoding and see that it overfits:
+print(
+    "Model without CV on training set: ",
+    model_no_cv.score(X_train_no_cv_encoding, y_train),
+)
+print(
+    "Model without CV on test set: ", model_no_cv.score(X_test_no_cv_encoding, y_test)
+)
+
+# %%
+# The ridge model overfits, because it assigns more weight to the extremely high
+# cardinality feature relative to the informative feature.
+coefs_no_cv = pd.Series(
+    model_no_cv.coef_, index=model_no_cv.feature_names_in_
+).sort_values()
+_ = coefs_no_cv.plot(kind="barh")
+
+# %%
+# Conclusion
+# ==========
+# This example demonstrates the importance of :class:`TargetEncoder`'s interval cross
+# validation. It is important to use :meth:`TargetEncoder.fit_transform` to encode
+# training data before passing it to a machine learning model. When a
+# :class:`TargetEncoder` is a part of a :class:`~sklearn.pipeline.Pipeline` and the
+# pipeline is fitted, the pipeline will correctly call
+# :meth:`TargetEncoder.fit_transform` and pass the encoding along.