Preventing Overfitting on Small Data

Scenario

You're training a supervised model with limited data, and the labels or features may be noisy. You want the model to generalize well instead of memorizing artifacts in the sample.

Question

What techniques do you use to prevent overfitting when working with small or highly noisy datasets?