Weights are numerical parameters in a machine learning model that determine the strength and significance of input features in producing output predictions. During the training process, algorithms adjust these weights based on the data to minimize the difference between predicted and actual values. Weights are fundamental to various model architectures, including neural networks, where they connect neurons in different layers. Each weight influences the output by scaling the corresponding input feature, enabling the model to learn complex patterns and relationships within the data. Proper weight initialization and optimization are crucial for effective model performance.