Model Accuracy refers to the measure of how well a predictive model performs in making correct predictions. It is calculated by comparing the number of correct predictions to the total number of predictions made. A higher accuracy percentage indicates that the model is more reliable in its predictions. In the context of machine learning, Model Accuracy is crucial for evaluating the effectiveness of algorithms. It helps developers understand how well their models are working and whether adjustments are needed. However, accuracy alone may not provide a complete picture, especially in cases with imbalanced datasets, where other metrics like precision and recall may also be important.