Example Concept: To improve model accuracy without overfitting, you can use techniques such as cross-validation, regularization, and feature selection. Cross-validation helps ensure the model performs well on unseen data by dividing the dataset into training and validation sets multiple times. Regularization techniques, like L1 or L2, add a penalty for larger coefficients, discouraging overly complex models. Feature selection involves choosing only the most relevant features, reducing noise and improving generalization.