Imbalanced data refers to a situation in which the classes within a dataset are disproportionately represented, with one class significantly outnumbering the others. This can lead to challenges in training machine learning models as they tend to be biased towards the majority class, resulting in poor performance on predicting the minority class. Addressing imbalanced data typically involves techniques such as resampling (over-sampling or under-sampling), using different evaluation metrics, and employing specialized algorithms designed to handle imbalanced datasets.