What is the purpose of cross-validation in model evaluation?

What is the purpose of cross-validation in model evaluation?

Step 1: Understand that when we create a model, we want it to perform well on new, unseen data.
Step 2: Realize that if a model learns too much from the training data, it may not work well on new data. This is called overfitting.
Step 3: Cross-validation is a technique used to test the model on different parts of the data.
Step 4: In cross-validation, we split the data into several smaller groups (called folds).
Step 5: We train the model on some of these groups and test it on the remaining group.
Step 6: We repeat this process multiple times, each time using a different group for testing.
Step 7: By doing this, we can see how well the model performs on different data and ensure it is not just memorizing the training data.

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