I've been working on trying to build a predictive model. I have 26 predictor variables (all continuous) for one response (n=52 in training set, also continuous). I think linear regression is the way to go and have read the relevant chapters of Applied Predictive Modeling and Chapter 6 of An Introduction to Statistical Learning (Linear Model Selection and Regularization).

Both books start simple and then introduce new possible models to use. As I read, each model sounds better than that last until they get to the lasso model, which sounds like exactly what I want. It will basically choose the necessary predictors variables (and get rid of the rest?) and build a relatively accurate model? While the other models mentioned sound good on some points, there is always a trade off.

Should I commit to learning the code and follow through with this? Or is lasso too good to be true?