@iglesias maybe because with increasing dimensions, the mixing of data points of 2 classes of Iris increases (near the boundary).  This mixing is least along the principal dimension chosen by PCA. While this is a plausible explanation, I am not very sure if this is the right one!
Should I put this explanation in the notebook?