What is the main goal of using cross-validation in model selection?

What is the main goal of using cross-validation in model selection?

Step 1: Understand that overfitting happens when a model learns the training data too well, including its noise and outliers.
Step 2: Realize that cross-validation is a technique used to evaluate how well a model will perform on unseen data.
Step 3: Learn that in cross-validation, the data is split into several parts (or folds).
Step 4: The model is trained on some parts of the data and tested on the remaining parts multiple times.
Step 5: By averaging the results from these tests, we get a better idea of the model's performance.
Step 6: The main goal of using cross-validation is to ensure that the model generalizes well to new data, reducing the risk of overfitting.

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