Which of the following describes overfitting in machine learning?

Overfitting in machine learning occurs when a model learns the training data too well, capturing noise and details that do not generalize to new, unseen data. This results in high performance during training, as the model has essentially memorized the training examples, but it fails to perform adequately when applied to different datasets. It demonstrates a lack of generalization, meaning the model is unable to make accurate predictions outside of the specific data it was trained on. The essence of overfitting is that the model is too complex relative to the amount and quality of training data available, leading to poor performance when presented with new examples.