Model-Based Methods are a class of algorithms in machine learning and control theory that utilize a model of the environment to make predictions and inform decision-making. These methods are particularly useful in scenarios where data is scarce or expensive to obtain, as they leverage prior knowledge to improve efficiency and accuracy.