What does cross-validation help to prevent?

What does cross-validation help to prevent?

Step 1: Understand that when we create a model, we train it on a specific set of data.
Step 2: Realize that if the model learns too much from this training data, it may not work well on new, unseen data.
Step 3: Know that this problem is called 'overfitting' - the model is too tailored to the training data.
Step 4: Learn that cross-validation is a technique used to test the model on different sets of data.
Step 5: Understand that by using cross-validation, we can see how well the model performs on data it hasn't seen before.
Step 6: Conclude that this helps to ensure the model is generalizing well and not just memorizing the training data.

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