Having only hundreds of training examples and fitting hundreds of models seems like a good recipe for overfitting. Have you noticed any issues with overfitting? Or maybe there is something that I am missing.
Cross validation helps to some extent. If you're fitting enough models, though, you can still trick yourself into thinking that some models are not overfitting, when they just happen to do well on your validation set by chance. But one of the things I look for is a smooth transition from underfitting to overfitting as the model capacity increases.
Then the other thing we do is probe the models so that we can understand what they're doing as much we can. I like to generate synthetic data and look at the transfer function of the model as I vary a particular parameter in my synthetic dataset, for example.