The Evidence Lower Bound (ELBO) is a crucial component in variational inference, serving as a tractable objective function that approximates the true posterior distribution by maximizing a lower bound on the model's log marginal likelihood. By optimizing the ELBO, one can effectively perform approximate Bayesian inference in complex probabilistic models where exact inference is computationally infeasible.