diff --git a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkTracker.scala b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkTracker.scala
index 76ab1284633b..b0f8cf9cd184 100644
--- a/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkTracker.scala
+++ b/sql/core/src/main/scala/org/apache/spark/sql/execution/streaming/WatermarkTracker.scala
@@ -63,7 +63,7 @@ case object MinWatermark extends MultipleWatermarkPolicy {
 }
 
 /**
- * Policy to choose the *min* of the operator watermark values as the global watermark value. So the
+ * Policy to choose the *max* of the operator watermark values as the global watermark value. So the
  * global watermark will advance if any of the individual operator watermarks has advanced.
  * In other words, in a streaming query with multiple input streams and watermarks defined on all
  * of them, the global watermark will advance as fast as the fastest input. So if there is watermark
@@ -108,10 +108,9 @@ case class WatermarkTracker(policy: MultipleWatermarkPolicy) extends Logging {
         }
     }
 
-    // Update the global watermark to the minimum of all watermark nodes.
-    // This is the safest option, because only the global watermark is fault-tolerant. Making
-    // it the minimum of all individual watermarks guarantees it will never advance past where
-    // any individual watermark operator would be if it were in a plan by itself.
+    // Update the global watermark accordingly to the chosen policy. To find all available policies
+    // and their semantics, please check the comments of
+    // `org.apache.spark.sql.execution.streaming.MultipleWatermarkPolicy` implementations.
     val chosenGlobalWatermark = policy.chooseGlobalWatermark(operatorToWatermarkMap.values.toSeq)
     if (chosenGlobalWatermark > globalWatermarkMs) {
       logInfo(s"Updating event-time watermark from $globalWatermarkMs to $chosenGlobalWatermark ms")