Custom regularization functions in TensorFlow allow for tailored constraints to enhance model performance. Implementing these as callable classes or functions provides flexibility in encoding domain knowledge into loss functions. Techniques include L1, L2, and smoothness penalties, enabling nuanced control over weight behaviors and aiding in tasks like feature selection and model compression.