In the context of machine learning, variance refers to the degree of variability in a model's predictions when it is trained on different subsets of data. A model with high variance pays too much attention to the training data, capturing noise and fluctuations rather than the underlying patterns, which can lead to overfitting. High variance often results in excellent performance on training data but poor generalization to new, unseen data. To combat high variance, techniques such as regularization, ensemble methods, and cross-validation can be employed. Understanding the balance between bias and variance is crucial for developing robust models that perform well across diverse datasets.