When running a generalized linear model, how do you decide which estimation to use? For example, maximum likelihood, forward selection, lasso, elastic net, etc. Would I prefer to keep all of my predictors and choose a method that doesn't select (i.e., maximum likelihood or ridge) when I only have a few predictors that I think are all important? Would I choose a method that selects when I have a lot of predictors and I'm trying to build a predictive model? When should I use a method that provides shrinkage?