Why is regularization important in machine learning, and what are some common techniques?

Regularization is crucial in machine learning to prevent overfitting, which occurs when a model learns to perform well on training data but fails to generalize to unseen data. Common techniques include L1 regularization (Lasso), which can lead to sparse models by driving some weights to zero, and L2 regularization (Ridge), which penalizes large weights to ensure smoother, more generalizable models. These methods help improve model performance by striking a balance between fitting the training data well and maintaining simplicity.