What is the role of regularization in model selection?

What is the role of regularization in model selection?

Step 1: Understand that in machine learning, we create models to make predictions based on data.
Step 2: Know that sometimes models can become too complex and fit the training data too closely, which is called overfitting.
Step 3: Realize that overfitting means the model may not perform well on new, unseen data.
Step 4: Learn that regularization is a technique used to prevent overfitting by adding a penalty to the model for having large coefficients (weights).
Step 5: Understand that this penalty encourages the model to keep the coefficients smaller and simpler.
Step 6: Recognize that by using regularization, the model can generalize better to new data, improving its performance.

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