The L1 penalty, also known as Lasso regularization, adds the absolute value of coefficients as a penalty term to the loss function, encouraging sparsity in the model by driving some coefficients to zero. This helps in feature selection and can prevent overfitting by simplifying the model, especially when dealing with high-dimensional data.