L1 and L2 regularization are helpful tools that make sure our models work well by stopping them from learning the wrong patterns, which is called overfitting. This is important in supervised learning, where we teach a model using known data.

L1 Regularization (Lasso)

• What it is: L1 regularization adds the absolute values of the model's weights into the loss function.
• What it does: It encourages simplicity by making some weights go to zero. This helps us pick the most important features to focus on.

L2 Regularization (Ridge)

• What it is: L2 regularization adds the squared values of the model's weights into the loss function.
• What it does: It keeps large weights in check but does not remove any features. This helps to simplify the model without losing information.

Both methods try to lower the loss function effectively. This leads to better results when our model is asked to predict new, unseen data.