Model-agnostic Methods are Techniques in machine learning that can be applied across different types of models, allowing for Flexibility and broad applicability without being tied to a Specific algorithm. These methods are particularly useful for interpretability, Feature importance analysis, and improving Model Transparency, as they work independently of the underlying model architecture.