The hardness of approximation refers to the difficulty of finding approximate solutions to optimization problems within a specific factor of the optimal solution, especially when exact solutions are computationally infeasible. It highlights the intrinsic limitations of approximation algorithms, often demonstrated through reductions and complexity-theoretic assumptions, such as NP-hardness and the Unique Games Conjecture.