Updates AD documentation with anomaly explanation (elastic#2262)

* Updates AD documentation with anomaly explanation.

* Further edits.

Author: szabosteve

docs/en/stack/ml/anomaly-detection/ml-buckets.asciidoc

@@ -37,6 +37,12 @@ In this example, you can see that some of the anomalies fall within the shaded
 blue area, which represents the bounds for the expected values. The bounds are
 calculated per bucket, but multi-bucket analysis is not limited by that scope.
 
+Both the **Anomaly Explorer** and the **Single Metric Viewer** contain an 
+**Anomalies** table that shows key details about each anomaly such as time, 
+typical and actual values, and probability. The **Anomaly explanation** section 
+helps you to interpret a given anomaly by providing further insights about its 
+type, impact, and score.
+
 If you have more than one {anomaly-job}, you can also obtain _overall bucket_
 results, which combine and correlate anomalies from multiple jobs into an
 overall score. When you view the results for job groups in {kib}, it provides

docs/en/stack/ml/get-started/images/ml-gs-job1-anomalies.jpg

@@ -94,6 +94,10 @@ or higher in the selected section of the timeline. If you are only interested in
 critical anomalies, for example, you can change the severity threshold for this
 table.
 
+The **Anomaly explanation** section gives you further insights about each 
+anomaly, such as its type and impact, to make it easier to interpret the job 
+results.
+
 You can optionally annotate your job results by drag-selecting a period of time
 in the **Single Metric Viewer** and adding a description. Annotations are notes
 that refer to events in a specific time period. They can be created by the