Covariance matrix estimation is crucial in various fields like finance, bioinformatics, and economics. This paper reviews methods for estimating covariance matrices when the matrix size is large compared to the sample size. Two main approaches are discussed: using structured assumptions for accurate estimation, and shrinking eigenvalues of sample covariance matrices to correct biases. The findings suggest that these methods can help in accurately estimating covariance matrices in high-dimensional data analysis.