Regularization is a crucial technique in machine learning aimed at preventing overfitting, which occurs when a model learns the noise in the training data rather than the underlying patterns. By adding a penalty term to the cost function, regularization constrains the model’s complexity, encouraging it to generalize better to unseen data. Common forms of regularization include L1 (Lasso) and L2 (Ridge) regularization, each applying different penalties to the model’s weights. This approach not only improves model performance but also enhances interpretability by reducing the risk of fitting to irrelevant features in the dataset.