Example Concept: To improve model accuracy without overfitting, you can use techniques such as cross-validation, regularization, and data augmentation. Cross-validation helps in assessing the model's performance on different subsets of the data, ensuring it generalizes well. Regularization techniques like L1 and L2 add a penalty to the loss function to discourage overly complex models. Data augmentation increases the diversity of the training data without collecting new data, which helps the model learn more robust features.