L2 Regularization, also known as Ridge Regression, is a technique used in machine learning to prevent overfitting by adding a penalty equivalent to the square of the magnitude of coefficients to the loss function. This penalty term discourages the model from fitting to noise in the training data by keeping the model weights small, thus promoting simpler models that generalize better to unseen data.