SHAP Values, or Shapley Additive Explanations, provide a unified measure of feature importance in machine learning models by attributing the change in prediction to each feature. They are grounded in cooperative game theory and ensure fair distribution of prediction contributions among features, making them a powerful tool for model interpretability and transparency.