Subset selection is a fundamental problem in various domains, where the goal is to choose an optimal subset of elements from a large candidate set under given objectives and constraints. Pareto Optimization for Subset Selection (POSS) and its variants, such as PORSS and TPOSS, have recently shown promising results by reformulating the problem as a two-objective optimization and solving it using simple evolutionary multi-objective optimization (EMO) algorithms. However, it remains unclear how these algorithms compare to other well-established EMO algorithms, including both classical and specialized approaches. In this paper, we conduct a benchmarking study involving nine EMO algorithms across two representative subset selection tasks. Our results show that TPOSS, a simple yet effective algorithm, consistently outperforms more complex classical and specialized EMO algorithms. These findings reveal that simpler EMO strategies can be surprisingly competitive and even superior in subset selection scenarios, offering valuable insights for designing future EMO algorithms tailored to such tasks.