What is the purpose of using regularization techniques in model selection?

What is the purpose of using regularization techniques in model selection?

Step 1: Understand that when we create a model, we want it to learn from the data.
Step 2: Sometimes, a model can learn too much from the training data, including noise or random fluctuations. This is called overfitting.
Step 3: Overfitting means the model performs well on training data but poorly on new, unseen data.
Step 4: Regularization techniques help prevent overfitting by adding a penalty to the model for having large coefficients (weights).
Step 5: This penalty encourages the model to keep the coefficients smaller, making it simpler and more generalizable.
Step 6: By using regularization, we aim to improve the model's performance on new data.

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