Example Concept: To enhance model accuracy without overfitting, you can use techniques such as regularization (like L1 or L2), cross-validation to ensure the model performs well on unseen data, and early stopping to prevent the model from learning noise. Additionally, using more data or data augmentation can help the model generalize better. Simplifying the model architecture or using dropout layers in neural networks can also reduce overfitting.