diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index a1fb638bb29db..ff591ba0c85c5 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -2545,22 +2545,23 @@ def log_loss(
     Returns
     -------
     loss : float
+        Log loss, aka logistic loss or cross-entropy loss.
 
     Notes
     -----
     The logarithm used is the natural logarithm (base-e).
 
+    References
+    ----------
+    C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer,
+    p. 209.
+
     Examples
     --------
     >>> from sklearn.metrics import log_loss
     >>> log_loss(["spam", "ham", "ham", "spam"],
     ...          [[.1, .9], [.9, .1], [.8, .2], [.35, .65]])
     0.21616...
-
-    References
-    ----------
-    C.M. Bishop (2006). Pattern Recognition and Machine Learning. Springer,
-    p. 209.
     """
     y_pred = check_array(y_pred, ensure_2d=False)
     check_consistent_length(y_pred, y_true, sample_weight)
diff --git a/sklearn/tests/test_docstrings.py b/sklearn/tests/test_docstrings.py
index cc5883f3acc4b..36ffe78ecfb14 100644
--- a/sklearn/tests/test_docstrings.py
+++ b/sklearn/tests/test_docstrings.py
@@ -35,7 +35,6 @@
     "sklearn.metrics._classification.brier_score_loss",
     "sklearn.metrics._classification.cohen_kappa_score",
     "sklearn.metrics._classification.jaccard_score",
-    "sklearn.metrics._classification.log_loss",
     "sklearn.metrics._plot.det_curve.plot_det_curve",
     "sklearn.metrics._plot.precision_recall_curve.plot_precision_recall_curve",
     "sklearn.metrics._ranking.coverage_error",