Some of the major issues in machine learning models include overfitting, where the model learns the training data too well and performs poorly on new data, and underfitting, where the model fails to learn enough from the training data. Other issues are bias, when the model makes assumptions about the data that lead to errors, and variance, where the model's performance is sensitive to small fluctuations in the training data. Additionally, handling high-dimensional data can be challenging, and models may struggle if the quality or quantity of training data is poor.