Bias and variance are inversely connected and It is nearly impossible practically to have an ML model with a low bias and a low variance. When we modify the ML algorithm to better fit a given data set, it will in turn lead to low bias but will increase the variance. This way, the model will fit with the data set while increasing the chances of inaccurate predictions. The same applies while creating a low variance model with a higher bias. Although it will reduce the risk of inaccurate predictions, the model will not properly match the data set. Hence it is a delicate balance between both biases and variance. But having a higher variance does not indicate a bad ML algorithm. Machine learning algorithms should be created accordingly so that they are able to handle some variance. Underfitting occurs when a model is unable to capture the underlying pattern of the data. Such models usually present with high bias and low variance.