diff --git a/doc/visualizations.rst b/doc/visualizations.rst
index 412dfc001fab1..e42be3a6db040 100644
--- a/doc/visualizations.rst
+++ b/doc/visualizations.rst
@@ -8,37 +8,86 @@ Scikit-learn defines a simple API for creating visualizations for machine
 learning. The key feature of this API is to allow for quick plotting and
 visual adjustments without recalculation. We provide `Display` classes that
 expose two methods for creating plots: `from_estimator` and
-`from_predictions`. The `from_estimator` method will take a fitted estimator
-and some data (`X` and `y`) and create a `Display` object. Sometimes, we would
-like to only compute the predictions once and one should use `from_predictions`
-instead. In the following example, we plot a ROC curve for a fitted support
-vector machine:
+`from_predictions`.
+
+The `from_estimator` method generates a `Display` object from a fitted estimator,
+input data (`X`, `y`), and a plot.
+The `from_predictions` method creates a `Display` object from true and predicted
+values (`y_test`, `y_pred`), and a plot.
+
+Using `from_predictions` avoids having to recompute predictions,
+but the user needs to take care that the prediction values passed correspond
+to the `pos_label`. For :term:`predict_proba`, select the column corresponding
+to the `pos_label` class while for :term:`decision_function`, revert the score
+(i.e. multiply by -1) if `pos_label` is not the last class in the
+`classes_` attribute of your estimator.
+
+The `Display` object stores the computed values (e.g., metric values or
+feature importance) required for plotting with Matplotlib. These values are the
+results derived from the raw predictions passed to `from_predictions`, or
+an estimator and `X` passed to `from_estimator`.
+
+Display objects have a plot method that creates a matplotlib plot once the display
+object has been initialized (note that we recommend that display objects are created
+via `from_estimator` or `from_predictions` instead of initialized directly).
+The plot method allows adding to an existing plot by passing the existing plots
+:class:`matplotlib.axes.Axes` to the `ax` parameter.
+
+In the following example, we plot a ROC curve for a fitted Logistic Regression
+model `from_estimator`:
 
 .. plot::
    :context: close-figs
    :align: center
 
     from sklearn.model_selection import train_test_split
-    from sklearn.svm import SVC
+    from sklearn.linear_model import LogisticRegression
     from sklearn.metrics import RocCurveDisplay
-    from sklearn.datasets import load_wine
+    from sklearn.datasets import load_iris
 
-    X, y = load_wine(return_X_y=True)
+    X, y = load_iris(return_X_y=True)
     y = y == 2  # make binary
-    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
-    svc = SVC(random_state=42)
-    svc.fit(X_train, y_train)
+    X_train, X_test, y_train, y_test = train_test_split(
+       X, y, test_size=.8, random_state=42
+    )
+    clf = LogisticRegression(random_state=42, C=.01)
+    clf.fit(X_train, y_train)
 
-    svc_disp = RocCurveDisplay.from_estimator(svc, X_test, y_test)
+    clf_disp = RocCurveDisplay.from_estimator(clf, X_test, y_test)
 
-The returned `svc_disp` object allows us to continue using the already computed
-ROC curve for SVC in future plots. In this case, the `svc_disp` is a
-:class:`~sklearn.metrics.RocCurveDisplay` that stores the computed values as
-attributes called `roc_auc`, `fpr`, and `tpr`. Be aware that we could get
-the predictions from the support vector machine and then use `from_predictions`
-instead of `from_estimator`. Next, we train a random forest classifier and plot
-the previously computed ROC curve again by using the `plot` method of the
-`Display` object.
+If you already have the prediction values, you could instead use
+`from_predictions` to do the same thing (and save on compute):
+
+
+.. plot::
+   :context: close-figs
+   :align: center
+
+    from sklearn.model_selection import train_test_split
+    from sklearn.linear_model import LogisticRegression
+    from sklearn.metrics import RocCurveDisplay
+    from sklearn.datasets import load_iris
+
+    X, y = load_iris(return_X_y=True)
+    y = y == 2  # make binary
+    X_train, X_test, y_train, y_test = train_test_split(
+       X, y, test_size=.8, random_state=42
+    )
+    clf = LogisticRegression(random_state=42, C=.01)
+    clf.fit(X_train, y_train)
+
+    # select the probability of the class that we considered to be the positive label
+    y_pred = clf.predict_proba(X_test)[:, 1]
+
+    clf_disp = RocCurveDisplay.from_predictions(y_test, y_pred)
+
+
+The returned `clf_disp` object allows us to add another curve to the already computed
+ROC curve. In this case, the `clf_disp` is a :class:`~sklearn.metrics.RocCurveDisplay`
+that stores the computed values as attributes called `roc_auc`, `fpr`, and `tpr`.
+
+Next, we train a random forest classifier and plot the previously computed ROC curve
+again by using the `plot` method of the `Display` object.
 
 .. plot::
    :context: close-figs
@@ -52,11 +101,12 @@ the previously computed ROC curve again by using the `plot` method of the
 
     ax = plt.gca()
     rfc_disp = RocCurveDisplay.from_estimator(rfc, X_test, y_test, ax=ax, alpha=0.8)
-    svc_disp.plot(ax=ax, alpha=0.8)
+    clf_disp.plot(ax=ax, alpha=0.8)
 
 Notice that we pass `alpha=0.8` to the plot functions to adjust the alpha
 values of the curves.
 
+
 .. rubric:: Examples
 
 * :ref:`sphx_glr_auto_examples_miscellaneous_plot_roc_curve_visualization_api.py`