Sublinear convergence describes a scenario in iterative algorithms where the rate of convergence to the solution is slower than linear, often implying that the error decreases at a diminishing rate as iterations proceed. This type of convergence is typically observed in algorithms where the computational cost or problem constraints limit the speed of approaching the optimal solution, making it crucial in assessing algorithm efficiency for large-scale problems.