Overfitting can be avoided by applying certain techniques and best practices that can enhance the quality and quantity of data, reduce the complexity of the model, and optimize the learning algorithm. Data cleaning and preprocessing are essential; this includes removing errors, outliers, duplicates, missing values, and inconsistencies in the data, as well as standardizing, scaling, encoding, and transforming it. Data augmentation and generation is also important; this involves creating or adding more data to the existing data through techniques such as flipping, rotating, cropping, noise injection or synthetic data generation. Feature selection and extraction is another key element; this includes selecting or extracting the most relevant and informative features from the data through filter methods, wrapper methods, embedded methods, autoencoders or dimensionality reduction. Model selection and evaluation is also necessary; this involves choosing or comparing the best model for the data and task through cross-validation, grid search, random search, Bayesian optimization or neural architecture search. Finally, regularization and dropout must be considered; this includes adding a penalty term to the loss function of the model or randomly dropping out some units or connections in the model to reduce its complexity. Early stopping and learning rate decay are also important; these involve stopping the training process when performance on validation data stops improving or reducing learning rate when performance on training data stops improving.