L1 and L2 regularization techniques, specifically LASSO and Ridge regression, are discussed to combat overfitting in machine learning models. Using a housing price dataset from Melbourne, the tutorial demonstrates how these regularizations can improve model accuracy by reducing complexity and penalizing large coefficients, resulting in better predictions on unseen data.