Add additional context to intro

Signed-off-by: Naarcha-AWS <naarcha@amazon.com>

_ml-commons-plugin/api.md

@@ -76,7 +76,7 @@ POST /_plugins/_ml/_train/kmeans?async=true
 
 **Synchronously**
 
-For synchronous responses, the API returns the model_id, which can be used to get or delete a task.
+For synchronous responses, the API returns the model_id, which can be used to get or delete a model.
 
 ```json
 {
@@ -87,7 +87,7 @@ For synchronous responses, the API returns the model_id, which can be used to ge
 
 **Asynchronously**
 
-For asynchronous responses, the API returns the task_id, which can be used to get or delete a tasks.
+For asynchronous responses, the API returns the task_id, which can be used to get or delete a task.
 
 ```json
 {

_ml-commons-plugin/index.md

@@ -10,10 +10,13 @@ has_toc: false
 
 ML Commons for OpenSearch eases the development of machine learning features by providing a set of common machine learning (ML) algorithms through transport and REST API calls. Those calls choose the right nodes and resources for each ML request and monitors ML tasks to ensure uptime. This allows you to leverage existing open-source ML algorithms and reduce the effort required to develop new ML features. 
 
-Models trained through the ML Commons plugin support model-based algorithms such as kmeans or Linear Regression. To get the best results, make sure you train your model first, then use the model to apply predictions. 
-
 Interaction with the ML commons plugin occurs through either the [REST API]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api) or [AD]({{site.url}}{{site.baseurl}}/ppl/commands#ad) and [kmeans]({{site.url}}{{site.baseurl}}/observability-plugin/ppl/commands#kmeans) PPL commands.
 
+Models [trained]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train) through the ML Commons plugin support model-based algorithms such as kmeans. After you've trained a model enough so that it meets your precision requirements, you can apply the model to [predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#predict) new data safely. 
+
+Should you not want to use a model, you can use the [Train and Predict]({{site.url}}{{site.baseurl}}/ml-commons-plugin/api#train-and-predict) API to test your model without having to evaluate the model's performance.
+
+
 ## Permissions
 
 There are two reserved user roles that can use of the ML commons plugin.