Parameter space refers to the multi-dimensional space defined by all possible values of parameters in a mathematical model or system. Each dimension corresponds to a specific parameter, and the points within this space represent different configurations or states of the model. For example, in machine learning, the parameters might include weights and biases that influence the model's predictions. Exploring parameter space is crucial for optimizing models, as it helps identify the best parameter values that minimize error or maximize performance. Techniques like grid search or random search are often used to navigate this space, allowing researchers to find optimal settings for algorithms such as neural networks or support vector machines.