Example Concept: To improve model accuracy without overfitting, you can use techniques such as cross-validation, regularization, and data augmentation. Cross-validation helps ensure that your model performs well on unseen data by training and validating on different subsets of the data. Regularization techniques like L1 and L2 add a penalty for larger coefficients, discouraging overly complex models. Data augmentation increases the diversity of the training dataset by applying random transformations, which helps the model generalize better to new data.