  We develop a robust convex algorithm to select the regularization parameter
in model selection. In practice this would be automated in order to save
practitioners time from having to tune it manually. In particular, we implement
and test the convex method for $K$-fold cross validation on ridge regression,
although the same concept extends to more complex models. We then compare its
performance with standard methods.
