Gradient techniques are optimization methods used in machine learning and statistics to minimize a function, often a loss function. These techniques rely on calculating the gradient, which is a vector that points in the direction of the steepest increase of the function. By moving in the opposite direction of the gradient, one can find the minimum value of the function, improving model performance. One common gradient technique is Gradient Descent, which iteratively updates parameters to reduce the loss. Variants like Stochastic Gradient Descent and Mini-batch Gradient Descent enhance efficiency by using subsets of data, making them suitable for large datasets and complex models.