scikit-learn · adrinjalali · Sep 21, 2023 · Jul 31, 2023 · Aug 4, 2023 · Aug 23, 2023
diff --git a/doc/modules/compose.rst b/doc/modules/compose.rst
@@ -5,14 +5,23 @@
 Pipelines and composite estimators
 ==================================
 
-Transformers are usually combined with classifiers, regressors or other
-estimators to build a composite estimator.  The most common tool is a
-:ref:`Pipeline <pipeline>`. Pipeline is often used in combination with
-:ref:`FeatureUnion <feature_union>` which concatenates the output of
-transformers into a composite feature space.  :ref:`TransformedTargetRegressor
-<transformed_target_regressor>` deals with transforming the :term:`target`
-(i.e. log-transform :term:`y`). In contrast, Pipelines only transform the
-observed data (:term:`X`).
+To build a composite estimator, transformers are usually combined with other
+transformers or with :term:`predictors` (such as classifiers or regressors).
+The most common tool used for composing estimators is a :ref:`Pipeline
+<pipeline>`. Pipelines require all steps except the last to be a
+:term:`transformer`. The last step can be anything, a transformer, a
+:term:`predictor`, or a clustering estimator which might have or not have a
+`.predict(...)` method. A pipeline exposes all methods provided by the last
+estimator: if the last step provides a `transform` method, then the pipeline
+would have a `transform` method and behave like a transformer. If the last step
+provides a `predict` method, then the pipeline would expose that method, and
+given a data :term:`X`, use all steps except the last to transform the data,
+and then give that transformed data to the `predict` method of the last step of
+the pipeline. `Pipeline` is often used in combination with :ref:`Column
+Transformer <column_transformer>` or :ref:`FeatureUnion <feature_union>` which
+concatenate the output of transformers into a composite feature space.
+:ref:`TransformedTargetRegressor <transformed_target_regressor>` deals with
+transforming the :term:`target` (i.e. log-transform :term:`y`).
 
 .. _pipeline:
 

diff --git a/sklearn/pipeline.py b/sklearn/pipeline.py
@@ -53,12 +53,15 @@ def check(self):
 
 class Pipeline(_BaseComposition):
     """
-    Pipeline of transforms with a final estimator.
+    A sequence of data transformers with an optional final predictor.
+
+    `Pipeline` allows you to sequentially apply a list of transformers to
+    preprocess the data and, if desired, conclude the sequence with a final
+    :term:`predictor` for predictive modeling.
 
-    Sequentially apply a list of transforms and a final estimator.
     Intermediate steps of the pipeline must be 'transforms', that is, they
     must implement `fit` and `transform` methods.
-    The final estimator only needs to implement `fit`.
+    The final :term:`estimator` only needs to implement `fit`.
     The transformers in the pipeline can be cached using ``memory`` argument.
 
     The purpose of the pipeline is to assemble several steps that can be
@@ -81,10 +84,11 @@ class Pipeline(_BaseComposition):
 
     Parameters
     ----------
-    steps : list of tuple
-        List of (name, transform) tuples (implementing `fit`/`transform`) that
-        are chained in sequential order. The last transform must be an
-        estimator.
+    steps : list of tuples
+        List of (name of step, estimator) tuples that are to be chained in
+        sequential order. To be compatible with the scikit-learn API, all steps
+        must define `fit`. All non-last steps must also define `transform`. See
+        :ref:`Combining Estimators <combining_estimators>` for more details.
 
     memory : str or object with the joblib.Memory interface, default=None
         Used to cache the fitted transformers of the pipeline. The last step
@@ -414,7 +418,7 @@ def _fit(self, X, y=None, routed_params=None):
     def fit(self, X, y=None, **params):
         """Fit the model.
 
-        Fit all the transformers one after the other and transform the
+        Fit all the transformers one after the other and sequentially transform the
         data. Finally, fit the transformed data using the final estimator.
 
         Parameters
@@ -478,9 +482,9 @@ def _can_fit_transform(self):
     def fit_transform(self, X, y=None, **params):
         """Fit the model and transform with the final estimator.
 
-        Fits all the transformers one after the other and transform the
-        data. Then uses `fit_transform` on transformed data with the final
-        estimator.
+        Fit all the transformers one after the other and sequentially transform
+        the data. Only valid if the final estimator either implements
+        `fit_transform` or `fit` and `transform`.
 
         Parameters
         ----------