diff --git a/docs/source/reference/pruners.rst b/docs/source/reference/pruners.rst
index cbd5c7b65a..3d3e3f2a03 100644
--- a/docs/source/reference/pruners.rst
+++ b/docs/source/reference/pruners.rst
@@ -5,6 +5,9 @@ optuna.pruners
 
 The :mod:`~optuna.pruners` module defines a :class:`~optuna.pruners.BasePruner` class characterized by an abstract :meth:`~optuna.pruners.BasePruner.prune` method, which, for a given trial and its associated study, returns a boolean value representing whether the trial should be pruned. This determination is made based on stored intermediate values of the objective function, as previously reported for the trial using :meth:`optuna.trial.Trial.report`. The remaining classes in this module represent child classes, inheriting from :class:`~optuna.pruners.BasePruner`, which implement different pruning strategies.
 
+.. warning::
+    Currently :mod:`~optuna.pruners` module is expected to be used only for single-objective optimization.
+
 .. seealso::
     :ref:`pruning` tutorial explains the concept of the pruner classes and a minimal example.
 
diff --git a/tutorial/10_key_features/003_efficient_optimization_algorithms.py b/tutorial/10_key_features/003_efficient_optimization_algorithms.py
index 0ac12996f1..b6970bd755 100644
--- a/tutorial/10_key_features/003_efficient_optimization_algorithms.py
+++ b/tutorial/10_key_features/003_efficient_optimization_algorithms.py
@@ -65,6 +65,7 @@
 # ------------------
 #
 # ``Pruners`` automatically stop unpromising trials at the early stages of the training (a.k.a., automated early-stopping).
+# Currently :mod:`~optuna.pruners` module is expected to be used only for single-objective optimization.
 #
 # Optuna provides the following pruning algorithms:
 #