Lasso is preferred over other methods like Ridge Regression and traditional regression techniques because it has the ability to produce sparse models, meaning it can effectively reduce the number of variables by forcing the coefficients of less important variables to be exactly zero. This is particularly beneficial in cancer research where datasets can be extremely large and complex.
