What is the purpose of cross-validation in the context of linear regression?

What is the purpose of cross-validation in the context of linear regression?

Step 1: Understand that linear regression is a method used to predict outcomes based on input data.
Step 2: Realize that when we create a model using data, we want to know how well it will perform on new, unseen data.
Step 3: Learn that cross-validation is a technique used to test the model's performance by splitting the data into parts.
Step 4: In cross-validation, we train the model on some parts of the data and test it on other parts.
Step 5: This process is repeated multiple times to ensure that the model is evaluated fairly.
Step 6: The results from cross-validation help us understand if our model is good at making predictions on new data.

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