A theorem that no learning algorithm is best for every possible task.
No Free Lunch is one pair of shoes for every sport. Flip-flops work at the pool, not in ice hockey.
It matters when you choose models or read benchmarks. For your real task, let the validation set decide.
Inductive Bias
No Free Lunch shows an algorithm wins by matching a problem well.
SLT
No Free Lunch is a learning theory result about algorithm limits.
Cross-Validation
No universal winner exists, so validation results must guide model choice.