diff --git a/examples/release_highlights/plot_release_highlights_1_2_0.py b/examples/release_highlights/plot_release_highlights_1_2_0.py
index e46c821f1f9e9..be44e0465f13c 100644
--- a/examples/release_highlights/plot_release_highlights_1_2_0.py
+++ b/examples/release_highlights/plot_release_highlights_1_2_0.py
@@ -68,6 +68,31 @@
 )
 hist_no_interact.fit(X, y)
 
+# %%
+# New and enhanced displays
+# -------------------------
+# :class:`~metrics.PredictionErrorDisplay` provides a way to analyze regression
+# models in a qualitative manner.
+import matplotlib.pyplot as plt
+from sklearn.metrics import PredictionErrorDisplay
+
+fig, axs = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
+PredictionErrorDisplay.from_estimator(
+    hist_no_interact, X, y, kind="actual_vs_predicted", ax=axs[0]
+)
+PredictionErrorDisplay.from_estimator(
+    hist_no_interact, X, y, kind="residual_vs_predicted", ax=axs[1]
+)
+
+# %%
+# :class:`~model_selection.LearningCurveDisplay` is now available to plot
+# results from :func:`~model_selection.learning_curve`.
+from sklearn.model_selection import LearningCurveDisplay
+
+LearningCurveDisplay.from_estimator(
+    hist_no_interact, X, y, cv=5, n_jobs=2, train_sizes=np.linspace(0.1, 1, 5)
+)
+
 # %%
 # Faster parser in :func:`~datasets.fetch_openml`
 # -----------------------------------------------