What is regularization in machine learning?

Regularization is a technique used to prevent overfitting by adding a penalty to the model’s complexity. Common regularization methods include:
L2 regularization (Ridge): Adds a penalty proportional to the square of the coefficients.
L1 regularization (Lasso): Adds a penalty proportional to the absolute value of the coefficients. Regularization helps the model generalize better by preventing it from fitting noise in the training data.