docs: address Ran's feedback

Signed-off-by: heitorlessa <lessa@amazon.co.uk>

Author: heitorlessa

docs/content/utilities/parser.mdx

@@ -48,15 +48,6 @@ class Order(BaseModel):
 
 These are simply Python classes that inherit from BaseModel. **Parser** enforces type hints declared in your model at runtime.
 
-<!-- TODO: We need a dedicated section to expand on these great features from Pydantic -->
-<!-- The advantage here is that they can be [recursive, dumped as JSON, JSON Schema, Dicts, have validation and more](https://pydantic-docs.helpmanual.io/usage/models/). -->
-
-<Note type="info">
-    <strong>Looking to auto-generate models from JSON, YAML, JSON Schemas, OpenApi, etc?</strong>
-    <br/><br/>
-    Use Koudai Aono's <a href="https://github.com/koxudaxi/datamodel-code-generator">data model code generation tool for Pydantic</a>
-</Note><br/>
-
 ## Parsing events
 
 You can parse inbound events using **event_parser** decorator, or the standalone `parse` function. Both are also able to parse either dictionary or JSON string as an input.
@@ -460,6 +451,56 @@ parse(model=UserModel, event=payload)
 </Note><br/>
 
 
+## Advanced use cases
+
+<Note type="info">
+    <strong>Looking to auto-generate models from JSON, YAML, JSON Schemas, OpenApi, etc?</strong>
+    <br/><br/>
+    Use Koudai Aono's <a href="https://github.com/koxudaxi/datamodel-code-generator">data model code generation tool for Pydantic</a>
+</Note><br/>
+
+There are number of advanced use cases well documented in Pydantic's doc such as creating [immutable models](https://pydantic-docs.helpmanual.io/usage/models/#faux-immutability), [declaring fields with dynamic values]((https://pydantic-docs.helpmanual.io/usage/models/#field-with-dynamic-default-value)) e.g. UUID, and [helper functions to parse models from files, str](https://pydantic-docs.helpmanual.io/usage/models/#helper-functions), etc.
+
+Two possible unknown use cases are Models and exception' serialization. Models have methods to [export them](https://pydantic-docs.helpmanual.io/usage/exporting_models/) as `dict`, `JSON`, `JSON Schema`, and Validation exceptions can be exported as JSON.
+
+```python:title=serializing_models_exceptions.py
+from aws_lambda_powertools.utilities import Logger
+from aws_lambda_powertools.utilities.parser import parse, BaseModel, ValidationError, validator
+
+logger = Logger(service="user")
+
+class UserModel(BaseModel):
+    username: str
+    password1: str
+    password2: str
+
+payload = {
+    "username": "universe",
+    "password1": "myp@ssword",
+    "password2": "repeat password"
+}
+
+def my_function():
+    try:
+        return parse(model=UserModel, event=payload)
+    except ValidationError as e:
+        logger.exception(e.json()) # highlight-line
+        return {
+            "status_code": 400,
+            "message": "Invalid username"
+        }
+
+User: UserModel = my_function()
+# highlight-start
+user_dict = User.dict()
+user_json = User.json()
+user_json_schema_as_dict = User.schema()
+user_json_schema_as_json = User.schema_json(indent=2)
+# highlight-end
+```
+
+These can be quite useful when manipulating models that later need to be serialized as inputs for services like DynamoDB, EventBridge, etc.
+
 ## FAQ
 
 **When should I use parser vs data_classes utility?**