Two new approaches to variable selection in regression are presented. The key idea in both approaches is to calibrate an existing tunable selection method in order to achieve desirable properties of selected models. Attention is restricted to forward selection for which the tuning parameter is the familiar alpha-to-enter. In the first approach, Noise Added Model Selection (NAMS), parametric bootstrap-like data sets are generated by incrementally adding noise to the response variable, and alpha is tuned by tracking the effect of added noise on selected models' mean squared errors for different alpha values. In the second approach, Variable Added Model Selection (VAMS), random phony predictor variables are added to the data set, and alpha is tuned by tracking the proportion of falsely included phony variables in the models selected for different alpha values.