Example Concept: To improve model accuracy without overfitting, you can use techniques such as cross-validation to ensure robust evaluation, regularization methods like L1 or L2 to penalize overly complex models, and data augmentation to increase the diversity of your training data. Additionally, feature selection can help by removing irrelevant or redundant features, and early stopping can prevent the model from learning noise in the data.