Convergence diagnostics are crucial in assessing whether a Markov Chain Monte Carlo (MCMC) simulation has reached a stable distribution, ensuring that the estimates derived are reliable. They help in identifying issues like autocorrelation and insufficient mixing, which can lead to incorrect inferences if not addressed properly.