diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index f7d002ac23743..ac488ac3af772 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -1904,6 +1904,23 @@ class after being classified as negative. This is the case when the
     return positive_likelihood_ratio, negative_likelihood_ratio
 
 
+@validate_params(
+    {
+        "y_true": ["array-like", "sparse matrix"],
+        "y_pred": ["array-like", "sparse matrix"],
+        "labels": ["array-like", None],
+        "pos_label": [Real, str, "boolean", None],
+        "average": [
+            StrOptions({"micro", "macro", "samples", "weighted", "binary"}),
+            None,
+        ],
+        "sample_weight": ["array-like", None],
+        "zero_division": [
+            Options(Real, {0, 1}),
+            StrOptions({"warn"}),
+        ],
+    }
+)
 def precision_score(
     y_true,
     y_pred,
diff --git a/sklearn/tests/test_public_functions.py b/sklearn/tests/test_public_functions.py
index 4e13bb46ef645..665fc8c7af98b 100644
--- a/sklearn/tests/test_public_functions.py
+++ b/sklearn/tests/test_public_functions.py
@@ -135,6 +135,7 @@ def _check_function_param_validation(
     "sklearn.metrics.mutual_info_score",
     "sklearn.metrics.pairwise.additive_chi2_kernel",
     "sklearn.metrics.precision_recall_fscore_support",
+    "sklearn.metrics.precision_score",
     "sklearn.metrics.r2_score",
     "sklearn.metrics.roc_curve",
     "sklearn.metrics.zero_one_loss",