Adversarial learning is a machine learning technique where models are trained to improve their robustness by generating and defending against adversarial examples, which are inputs intentionally designed to cause the model to make mistakes. This approach is crucial for enhancing the security and reliability of AI systems, especially in applications like image recognition and autonomous driving.