the opposite of "parameter tuning is bad"

"With more parameters data sparsity becomes an issue again, but with proper smoothing the models are usually more accurate than the original models. Thus, no matter how much data one has, smoothing can almost always help performace, and for a relatively small effort."

"Adding free parameters to an algorithm and optimizing these parameters on held-out data can improve performance."

http://nlp.stanford.edu/~wcmac/papers/20050421-smoothing-tutorial.pdf