TL;DR: Overfitting ruining your model? See how Ridge, Lasso and Elastic Net work, and how alpha and feature scaling keep models accurate on new data.

In the world of machine learning , a little humility goes a long way, especially for your models. That’s where regularization steps in. It’s a technique designed to prevent overfitting , which, as we’ve seen, happens when a model gets a little too enthusiastic about fitting the training data, capturing noise, quirks, and coincidences instead of actual patterns.

Regularization works by adding a penalty term to the model’s loss function. This penalty discourages complexity, shrinking large coefficients and promoting simpler models that generalize better to new, unseen data.…