Flask is a micro web framework written in Python that can be used to develop and deploy web applications, including those that serve Machine Learning models. Here are the steps to deploy a Machine Learning model using Flask:

1. Train and save your Machine Learning model: First, you need to train and save your Machine Learning model using a Python library such as scikit-learn or TensorFlow.

2. Create a Flask application: You can create a new Flask application or use an existing one. Flask allows you to define routes that map to specific URLs, so you can create a route that will handle requests to your Machine Learning model.

3. Load the Machine Learning model: Load the trained Machine Learning model into your Flask application. You can use a library such as joblib or pickle to load the saved model.

4. Define a route to handle requests: Define a route in your Flask application that will handle requests to your Machine Learning model. You can define the route using the @app.route decorator in Flask.

5. Parse input data: In the route, parse the input data that is sent to the Machine Learning model. Depending on the type of input data, you may need to use a library such as pandas to parse and clean the data.

6. Make predictions using the Machine Learning model: Once the input data is parsed, use the loaded Machine Learning model to make predictions.

7. Return predictions to the user: Finally, return the predictions to the user in a format that makes sense for your application.

Overall, Flask provides a simple and effective way to deploy Machine Learning models as web services. It allows you to easily handle incoming requests, parse input data, make predictions using your Machine Learning model, and return the results to the user.