The main challenge in machine learning models is achieving generalization, which refers to the model's ability to perform well on unseen data. Models can become overfitted when they learn noise and fluctuations in training data instead of the underlying patterns, resulting in poor performance on new datasets. To combat this, techniques such as cross-validation, regularization, and using larger, more diverse datasets can be employed. Balancing the trade-off between model complexity and performance is crucial for developing robust machine learning systems that effectively address real-world problems and datasets.