The LASSO is a short form for the Least Absolute Shrinkage and Selection Operator model, a regression analysis method. The LASSO Model performs variable selection and regularization to improve the predictive accuracy & interpretability of a statistical model. It was Introduced by Robert Tibshirani in 1996. The LASSO model aims to overcome the limitations of traditional regression techniques. It does this by imposing a constraint on the sum of the absolute values of the coefficients. This constraint shrinks some coefficients and materializes others to zero, effectively selecting a simpler model that avoids overfitting. The strength of the shrinkage (and thus the number of coefficients driven to zero) is controlled by a tuning parameter, λ. By adjusting λ, LASSO allows for a balance between fitting the data well and keeping the model complexity minimal. This makes it it particularly useful in certain scenarios where the number of predictor variables exceeds the number of observations or when there is a need to identify a relevant subset of predictors out of a large set.