Convergence is when the gradient descent algorithm successfully minimises its cost function to an optimal level. Gradient Descent Algorithm tries to minimise the cost function through the algorithm parameters. However, it can land on any of the optimal points and not necessarily the one that has a global or local optimum point. One reason for not having optimal convergence is the step size. A more significant step size results in more oscillations and may divert from the global optimal. Hence, gradient descent may not always converge on the best feature, but it still lands on the nearest feature point.