The "curse of dimensionality" refers to the challenges that arise when analyzing data in high-dimensional spaces. As the number of dimensions increases, the volume of the space grows exponentially, making data points sparse. This sparsity can lead to difficulties in finding patterns and relationships, as well as increased computational costs. In machine learning, the curse of dimensionality can hinder model performance. For instance, algorithms like k-nearest neighbors may struggle to find meaningful neighbors in high dimensions, leading to overfitting. Consequently, dimensionality reduction techniques, such as Principal Component Analysis (PCA), are often employed to mitigate these issues.