Lasso Regression (Least Absolute Shrinkage and Selection Operator) is a type of linear regression technique that performs both variable selection and regularization to enhance the prediction accuracy and interpretability of the statistical model it produces. By imposing a constraint on the model parameters, lasso tends to produce some coefficients that are exactly zero, effectively selecting a simpler model that does not include those variables.