Lasso regression is a linear regression technique that uses L1 regularization to penalize the absolute size of the coefficients, effectively shrinking some of them to zero, which enables feature selection and promotes sparsity in the model. This helps in preventing overfitting and improving the interpretability of the model by identifying the most significant predictors.