The curse of high dimensionality is the empirical phenomenon that in high dimensional space, concepts of proximity, distance, and/or nearest neighbor lose qualitative meaning. This problem is more severe for Euclidean distance than for Manhattan distance, hence why Manhattan distance is preferred for high dimensional data.