Example Concept: To improve model accuracy without overfitting, you can use techniques such as regularization, cross-validation, and data augmentation. Regularization methods like L1 and L2 add penalties to the loss function to discourage overly complex models. Cross-validation helps ensure that the model performs well on unseen data by splitting the dataset into training and validation sets multiple times. Data augmentation artificially increases the size of the training dataset by applying transformations, which helps the model generalize better.