KerasWrapperModel` is a one-line wrapper that enables TensorFlow/Keras models to be used with scikit-learn's rich ecosystem, including features like Pipeline and GridSearch. This allows users to leverage the strengths of both frameworks without having to rewrite their code or compromise on model architecture. The `CleanLearning` utility can also be applied to any sklearn-compatible model to identify label issues in the dataset and train a more robust version of the same model. By making neural networks sklearn-compatible, developers can tap into the full range of scikit-learn's functionality, including hyperparameter tuning and data preprocessing, to improve their models' performance and accuracy.